Triple
T12330305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Perry (snooker player) |
E293940
|
entity |
| Predicate | hasProfessionalCareerSpan |
P18004
|
FINISHED |
| Object | over 30 years |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: over 30 years | Statement: [Joe Perry (snooker player), hasProfessionalCareerSpan, over 30 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalCareerSpan Context triple: [Joe Perry (snooker player), hasProfessionalCareerSpan, over 30 years]
-
A.
hasCareerSpanCoverage
Indicates that one entity’s coverage, record, or data extends across the full duration of another entity’s career span.
-
B.
activeYearsInCareer
chosen
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
C.
hasCareerGamesPlayed
Indicates the total number of games an entity has played over the course of its entire career.
-
D.
careerSeasons
Indicates the number or set of seasons during which an entity actively participated in a particular career or professional role.
-
E.
playedCareerStartYear
Indicates the calendar year in which an entity’s playing career (such as a professional or competitive role) began.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6ab6ae0dc8190b1522a9c1c55c114 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f634ee08190b4f533505d402219 |
completed | April 10, 2026, 6:20 p.m. |
| PD | Predicate disambiguation | batch_69d93ec5be788190b82d2edc6a0f1095 |
completed | April 10, 2026, 6:17 p.m. |
Created at: April 8, 2026, 9:53 p.m.